The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task performance. However, existing dataset p...
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Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dat...
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Fault localization (FL) techniques gather trace information as input data and analyze it to identify the relationship between program statements and failures. Therefore, the input trace matrix is essential for fault l...
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Fault localization (FL) techniques gather trace information as input data and analyze it to identify the relationship between program statements and failures. Therefore, the input trace matrix is essential for fault localization. However, the current trace matrix faces two main challenges. Firstly, the occurrences of coincidental correctness (CC), which refer to the execution of faulty statements that lead to correct program output, adversely impact the effectiveness of FL. Secondly, the significant disparity in the number of failing and passing test cases poses a data imbalance problem for fault localization. To overcome these issues, we propose TRAIN: a Two-stage tRace mAtrix optImizatioN method for fault localization. In the first stage of optimization, TRAIN leverages an improved cluster analysis to identify and exclude the CC tests to optimize the trace matrix. Subsequently, in the second stage, TRAIN utilizes data augmentation to enhance the failing test cases to further balance the trace matrix. The optimized trace matrix is then used as input data in the FL pipeline to locate the faulty statements. Through extensive experiments conducted on 330 faulty versions of nine large-sized programs (obtained from Defects4J, ManyBugs, and SIR) using six state-of-the-art FL methods, TRAIN demonstrates remarkable improvements in FL effectiveness.
It is challenging to use unsupervised machine translation models to generate ancient poems. The current method has solved the problems of Under-translation and Over-translation caused by the huge length difference bet...
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It is challenging to use unsupervised machine translation models to generate ancient poems. The current method has solved the problems of Under-translation and Over-translation caused by the huge length difference between the translated sentence pairs. However, the above method lacks guidance in generating intermediate vectors, and the denoising ability of the model is very poor. In this paper, we guide vector space distribution during training to improve the quality of the generated ancient poems and the convergence speed of the model. We also introduce the target language information while adding noise, which effectively avoids the recurrence of the Under-translation problem while improving the model's denoising ability. Experiment results on the VP dataset show that our model obtains state-of-the-art results with faster convergence speed. In addition to the BLEU scores, we also made a comparative analysis of ancient poetry sentences generated by different models. The analysis results show that the optimization method proposed in this paper is indeed helpful for generating high-quality ancient poems.
Offline preference-based reinforcement learning (PbRL) offers an effective solution to overcome the challenges associated with designing rewards and the high costs of online interactions. Previous studies mainly focus...
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The problem of positioning of user equipment (UE) using LTE mobile communication network has become highly relevant in recent years, especially for GNSS denied environment. The object of this research is a software-de...
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The Internet of Things (IoT) enables all devices to sense, communicate, which has given rise to the evolution of traditional educational planning. Benefiting from this revolution, a new paradigm, named smart campus ca...
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Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning syst...
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In the distributed computing environment, it is important to efficiently achieve secure access to data. One of the widely applied encryption mechanisms is the multiauthority attribute-based encryption (ABE). However, ...
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In the distributed computing environment, it is important to efficiently achieve secure access to data. One of the widely applied encryption mechanisms is the multiauthority attribute-based encryption (ABE). However, most existing multiauthority setting schemes were relied on bilinear pairings, which causes the system to bear a relatively large burden in computation overhead. In this article, we proposes a decentralized multiauthority key-policy ABE scheme without bilinear pairings, its security is relied solely on the decisional Diffie-Hellman assumption. It removes the trusted central authority and prevents user collusion attacks. Except for the global coordination between the attribute authorities, any party can simply play the part of a standard attribute authority by generating public keys and issuing corresponding decryption key components for users. The proposed scheme provides heightened security and efficiency compared to the current pairing-free multiauthority ABE scheme, as well as superior computational efficiency when compared to those utilizing bilinear pairings.
Opensource communities utilize issues to promote knowledge sharing and discussions among developers. However, as the community scales, the number of issues increases dramatically. To ensure efficient circulation of is...
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Opensource communities utilize issues to promote knowledge sharing and discussions among developers. However, as the community scales, the number of issues increases dramatically. To ensure efficient circulation of issues and improve issues utilization, it is crucial to recommend appropriate issues to the developers. Nevertheless, developers' participation in resolving issues varies depending on their levels of expertise, leading to long-tail and cold-start problems. Additionally, interactions between developers and issues exhibit diverse topological modalities, which presents a challenge for existing recommendation models which are often built on a single type of embedding space, leading to suboptimal performance. To capture complex topological information, we propose the cross-space topological contrastive learning for knowledge graph-aware issue recommendation method. It combines different sparse interaction signals from collaborative filtering and knowledge graph information in Euclidean space and hyperbolic space for dual-space information aggregation. By performing intra-space contrastive learning between multi-hop subgraphs within each space, the contribution of CF signals and KG information can be effectively balanced. Cross-space contrastive learning avoids the occurrence of representation shift in a single space. CTCK alleviates the noise generated during KG propagation and increases consistency between the representations in both spaces. Its effectiveness is further enhanced with our proprietary issue knowledge graph (ISSUEKG), which can be used as auxiliary information to alleviate the long-tail problem. Through extensive experiments on a real-world dataset, we demonstrate that CTCK significantly outperforms 12 state-of-the-art baselines, beating the best method by 4.74, 7.36, and 2.86% on average in terms of AUC, F1-score, and accuracy, respectively.
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